Search Results for author: Can Cui

Found 62 papers, 12 papers with code

QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning

no code implementations20 Dec 2024 Xinyang Tong, Pengxiang Ding, Donglin Wang, Wenjie Zhang, Can Cui, Mingyang Sun, Yiguo Fan, Han Zhao, Hongyin Zhang, Yonghao Dang, Siteng Huang, Shangke Lyu

This paper addresses the inherent inference latency challenges associated with deploying multimodal large language models (MLLM) in quadruped vision-language-action (QUAR-VLA) tasks.

Language Modeling Language Modelling

Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation

no code implementations12 Dec 2024 Bofang Jia, Pengxiang Ding, Can Cui, Mingyang Sun, Pengfang Qian, Siteng Huang, Zhaoxin Fan, Donglin Wang

Visual-motor policy learning has advanced with architectures like diffusion-based policies, known for modeling complex robotic trajectories.

Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology

no code implementations25 Nov 2024 Lining Yu, Mengmeng Yin, Ruining Deng, Quan Liu, Tianyuan Yao, Can Cui, Junlin Guo, Yu Wang, Yaohong Wang, Shilin Zhao, Haichun Yang, Yuankai Huo

In this study, we leverage the Glo-In-One toolkit to version 2 with fine-grained segmentation capabilities, curating 14 distinct labels for tissue regions, cells, and lesions across a dataset of 23, 529 annotated glomeruli across human and mouse histopathology data.

Lesion Segmentation Segmentation +2

On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation

no code implementations17 Nov 2024 Can Cui, Zichong Yang, Yupeng Zhou, Juntong Peng, Sung-Yeon Park, Cong Zhang, Yunsheng Ma, Xu Cao, Wenqian Ye, Yiheng Feng, Jitesh Panchal, Lingxi Li, Yaobin Chen, Ziran Wang

Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while maintaining safety and comfort standards.

Autonomous Vehicles Natural Language Understanding +1

Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Simulation, and Real-Vehicle Experiment

no code implementations20 Oct 2024 Can Cui, Yunsheng Ma, Zichong Yang, Yupeng Zhou, Peiran Liu, Juanwu Lu, Lingxi Li, Yaobin Chen, Jitesh H. Panchal, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Ziran Wang

With the broader usage and highly successful development of Large Language Models (LLMs), there has been a growth of interest and demand for applying LLMs to autonomous driving technology.

Autonomous Driving Decision Making +4

ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification

1 code implementation30 Sep 2024 Can Cui, Siteng Huang, Wenxuan Song, Pengxiang Ding, Min Zhang, Donglin Wang

To address the occlusion issues in person Re-Identification (ReID) tasks, many methods have been proposed to extract part features by introducing external spatial information.

Decoder Occluded Person Re-Identification

Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis

no code implementations28 Jul 2024 Honglin Li, Yusuan Sun, Chenglu Zhu, Yunlong Zhang, Shichuan Zhang, Zhongyi Shui, Pingyi Chen, Jingxiong Li, Sunyi Zheng, Can Cui, Lin Yang

Though computer-aided automated diagnostic models can serve as strong complement for pathologists, their effectiveness is hampered by the paucity of extensive and detailed annotations, coupled with the limited interpretability and robustness.

GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model

no code implementations25 Jul 2024 Lining Yu, Mengmeng Yin, Ruining Deng, Quan Liu, Tianyuan Yao, Can Cui, Yitian Long, Yu Wang, Yaohong Wang, Shilin Zhao, Haichun Yang, Yuankai Huo

To answer this question, we introduced GLAM, a deep learning study for fine-grained segmentation of human kidney lesions using a mouse model, addressing mouse-to-human transfer learning, by evaluating different learning strategies for segmenting human pathological lesions using zero-shot transfer learning and hybrid learning by leveraging mouse samples.

Lesion Segmentation Segmentation +1

Dataset Distillation in Medical Imaging: A Feasibility Study

no code implementations19 Jul 2024 Muyang Li, Can Cui, Quan Liu, Ruining Deng, Tianyuan Yao, Marilyn Lionts, Yuankai Huo

Our extensive experiments across multiple medical datasets reveal that data distillation can significantly reduce dataset size while maintaining comparable model performance to that achieved with the full dataset, suggesting that a small, representative sample of images can serve as a reliable indicator of distillation success.

Dataset Distillation Medical Image Analysis

HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization

no code implementations3 Jul 2024 Yucheng Tang, Yufan He, Vishwesh Nath, Pengfeig Guo, Ruining Deng, Tianyuan Yao, Quan Liu, Can Cui, Mengmeng Yin, Ziyue Xu, Holger Roth, Daguang Xu, Haichun Yang, Yuankai Huo

In this paper, we propose the holistic histopathology (HoloHisto) segmentation method to achieve end-to-end segmentation on gigapixel WSIs, whose maximum resolution is above 80, 000$\times$70, 000 pixels.

4k Image Segmentation +3

mTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis

no code implementations28 May 2024 Quan Liu, Ruining Deng, Can Cui, Tianyuan Yao, Vishwesh Nath, Yucheng Tang, Yuankai Huo

Multi-modal learning adeptly integrates visual and textual data, but its application to histopathology image and text analysis remains challenging, particularly with large, high-resolution images like gigapixel Whole Slide Images (WSIs).

Survival Prediction whole slide images

Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

1 code implementation CVPR 2024 Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang

In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object.

Autonomous Vehicles motion prediction

Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications

no code implementations11 Mar 2024 Can Cui, Imran Ahamad Sheikh, Mostafa Sadeghi, Emmanuel Vincent

Past studies on end-to-end meeting transcription have focused on model architecture and have mostly been evaluated on simulated meeting data.

Action Detection Activity Detection +2

Improving Graph Contrastive Learning via Adaptive Positive Sampling

no code implementations CVPR 2024 Jiaming Zhuo, Feiyang Qin, Can Cui, Kun fu, bingxin niu, Mengzhu Wang, Yuanfang Guo, Chuan Wang, Zhen Wang, Xiaochun Cao, Liang Yang

Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture tailored for graphs has shown notable potential for mitigating label scarcity.

Contrastive Learning Self-Supervised Learning

MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding

1 code implementation CVPR 2024 Xu Cao, Tong Zhou, Yunsheng Ma, Wenqian Ye, Can Cui, Kun Tang, Zhipeng Cao, Kaizhao Liang, Ziran Wang, James M. Rehg, Chao Zheng

Specifically we annotate and leverage large-scale broad-coverage traffic and map data extracted from huge HD map annotations and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data.

Autonomous Driving Instruction Following +2

Personalized Autonomous Driving with Large Language Models: Field Experiments

no code implementations14 Dec 2023 Can Cui, Zichong Yang, Yupeng Zhou, Yunsheng Ma, Juanwu Lu, Lingxi Li, Yaobin Chen, Jitesh Panchal, Ziran Wang

We also validate that the proposed memory module considers personalized preferences and further reduces the takeover rate by up to 65. 2% compared with those without a memory module.

Autonomous Driving Language Modelling +3

A Survey on Multimodal Large Language Models for Autonomous Driving

1 code implementation21 Nov 2023 Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng

We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.

Autonomous Driving

MACP: Efficient Model Adaptation for Cooperative Perception

1 code implementation25 Oct 2023 Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao, Xu Cao, Wenqian Ye, Ziran Wang

We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.

model

End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis

no code implementations16 Oct 2023 Can Cui, Imran Ahamad Sheikh, Mostafa Sadeghi, Emmanuel Vincent

We present an end-to-end multichannel speaker-attributed automatic speech recognition (MC-SA-ASR) system that combines a Conformer-based encoder with multi-frame crosschannel attention and a speaker-attributed Transformer-based decoder.

Automatic Speech Recognition Decoder +3

Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

no code implementations12 Oct 2023 Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang

The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation.

Autonomous Driving Decision Making

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

no code implementations3 Jul 2023 Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.

Anomaly Detection

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

no code implementations31 May 2023 Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang, Lucas W. Remedios, Saydolimkhon Agzamkhodjaev, Zuhayr Asad, Quan Liu, Can Cui, Yaohong Wang, Yihan Wang, Yucheng Tang, Haichun Yang, Yuankai Huo

The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.

Cell Segmentation Image Segmentation +3

Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion

1 code implementation27 May 2023 Can Cui, Yunsheng Ma, Juanwu Lu, Ziran Wang

Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions.

3D Object Detection object-detection +1

Exploring shared memory architectures for end-to-end gigapixel deep learning

no code implementations24 Apr 2023 Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios, Karthik Ramadass, Aravind Krishnan, Ruining Deng, Can Cui, Shunxing Bao, Lori A. Coburn, Yuankai Huo, Bennett A. Landman

The M1 Ultra SoC was able to train the model directly on gigapixel images (16000$\times$64000 pixels, 1. 024 billion pixels) with a batch size of 1 using over 100 GB of unified memory for the process at an average speed of 1 minute and 21 seconds per batch with Tensorflow 2/Keras.

Deep Learning whole slide images

CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation

no code implementations30 Mar 2023 Wenqiao Zhang, Changshuo Liu, Can Cui, Beng Chin Ooi

In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two \emph{key subproblems}: \emph{robust domain adaptation (DA) learning} and \emph{maximal cross-domain data utilization}.

Domain Adaptation Semi-supervised Domain Adaptation

Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation

1 code implementation27 Jun 2022 Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jun Long, Zuhayr Asad, R. Michael Womick, Zheyu Zhu, Agnes B. Fogo, Shilin Zhao, Haichun Yang, Yuankai Huo

The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining.

Image Segmentation Segmentation +1

Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review

no code implementations25 Mar 2022 Can Cui, Haichun Yang, Yaohong Wang, Shilin Zhao, Zuhayr Asad, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice.

Decision Making Prognosis

Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data

no code implementations8 Mar 2022 Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong WangShilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo

Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).

Computational Efficiency Prediction +1

ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

1 code implementation7 Mar 2022 Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, Ipek Oguz

Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.

Lesion Segmentation

Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion

no code implementations25 Jan 2022 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Segmentation Unsupervised Domain Adaptation

Learned Coarse Models for Efficient Turbulence Simulation

1 code implementation31 Dec 2021 Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez

We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.

Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing

no code implementations22 Nov 2021 Thomas Leonard, Samuel Liu, Mahshid Alamdar, Can Cui, Otitoaleke G. Akinola, Lin Xue, T. Patrick Xiao, Joseph S. Friedman, Matthew J. Marinella, Christopher H. Bennett, Jean Anne C. Incorvia

In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain.

Learned Simulators for Turbulence

no code implementations ICLR 2022 Kim Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez

We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

no code implementations13 Sep 2021 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Segmentation Unsupervised Domain Adaptation

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

no code implementations9 Jul 2021 Dewei Hu, Can Cui, Hao Li, Kathleen E. Larson, Yuankai K. Tao, Ipek Oguz

We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space.

Retinal Vessel Segmentation Segmentation

Generalizing Nucleus Recognition Model in Multi-source Images via Pruning

no code implementations6 Jul 2021 Jiatong Cai, Chenglu Zhu, Can Cui, Honglin Li, Tong Wu, Shichuan Zhang, Lin Yang

In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains.

Domain Generalization Prognosis

AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment

no code implementations30 Mar 2021 Can Cui, Wei Wang, Meihui Zhang, Gang Chen, Zhaojing Luo, Beng Chin Ooi

In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes.

AutoML Stock Prediction

Asynchronous Multi-View SLAM

no code implementations17 Jan 2021 Anqi Joyce Yang, Can Cui, Ioan Andrei Bârsan, Raquel Urtasun, Shenlong Wang

Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice.

Sensor Modeling

Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

no code implementations11 Nov 2020 Wesley H. Brigner, Naimul Hassan, Xuan Hu, Christopher H. Bennett, Felipe Garcia-Sanchez, Can Cui, Alvaro Velasquez, Matthew J. Marinella, Jean Anne C. Incorvia, Joseph S. Friedman

This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track.

Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation

no code implementations3 Nov 2020 Han Liu, Can Cui, Dario J. Englot, Benoit M. Dawant

Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large.

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